PMID- 23620757
OWN - NLM
STAT- MEDLINE
DCOM- 20131111
LR  - 20211021
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 8
IP  - 4
DP  - 2013
TI  - Pharmacointeraction network models predict unknown drug-drug interactions.
PG  - e61468
LID - 10.1371/journal.pone.0061468 [doi]
LID - e61468
AB  - Drug-drug interactions (DDIs) can lead to serious and potentially lethal adverse 
      events. In recent years, several drugs have been withdrawn from the market due to 
      interaction-related adverse events (AEs). Current methods for detecting DDIs rely 
      on the accumulation of sufficient clinical evidence in the post-market stage - a 
      lengthy process that often takes years, during which time numerous patients may 
      suffer from the adverse effects of the DDI. Detection methods are further 
      hindered by the extremely large combinatoric space of possible drug-drug-AE 
      combinations. There is therefore a practical need for predictive tools that can 
      identify potential DDIs years in advance, enabling drug safety professionals to 
      better prioritize their limited investigative resources and take appropriate 
      regulatory action. To meet this need, we describe Predictive Pharmacointeraction 
      Networks (PPINs) - a novel approach that predicts unknown DDIs by exploiting the 
      network structure of all known DDIs, together with other intrinsic and taxonomic 
      properties of drugs and AEs. We constructed an 856-drug DDI network from a 2009 
      snapshot of a widely-used drug safety database, and used it to develop PPIN 
      models for predicting future DDIs. We compared the DDIs predicted based solely on 
      these 2009 data, with newly reported DDIs that appeared in a 2012 snapshot of the 
      same database. Using a standard multivariate approach to combine predictors, the 
      PPIN model achieved an AUROC (area under the receiver operating characteristic 
      curve) of 0.81 with a sensitivity of 48% given a specificity of 90%. An analysis 
      of DDIs by severity level revealed that the model was most effective for 
      predicting "contraindicated" DDIs (AUROC = 0.92) and less effective for "minor" 
      DDIs (AUROC = 0.63). These results indicate that network based methods can be 
      useful for predicting unknown drug-drug interactions.
FAU - Cami, Aurel
AU  - Cami A
AD  - Division of Emergency Medicine, Boston Children's Hospital, Boston, 
      Massachusetts, USA. aurel.cami@childrens.harvard.edu
FAU - Manzi, Shannon
AU  - Manzi S
FAU - Arnold, Alana
AU  - Arnold A
FAU - Reis, Ben Y
AU  - Reis BY
LA  - eng
GR  - R01 GM089731/GM/NIGMS NIH HHS/United States
GR  - R01 GM085421/GM/NIGMS NIH HHS/United States
GR  - R01 LM009879/LM/NLM NIH HHS/United States
GR  - R01 GM89731/GM/NIGMS NIH HHS/United States
GR  - R00 LM011014/LM/NLM NIH HHS/United States
GR  - K99 LM011014/LM/NLM NIH HHS/United States
GR  - 1K99LM011014-01/LM/NLM NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
DEP - 20130419
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
SB  - IM
MH  - *Drug Interactions
MH  - Drug-Related Side Effects and Adverse Reactions
MH  - Humans
MH  - *Models, Theoretical
MH  - ROC Curve
PMC - PMC3631217
COIS- Competing Interests: The authors have declared that no competing interests exist.
EDAT- 2013/04/27 06:00
MHDA- 2013/11/12 06:00
PMCR- 2013/04/19
CRDT- 2013/04/27 06:00
PHST- 2012/11/19 00:00 [received]
PHST- 2013/03/11 00:00 [accepted]
PHST- 2013/04/27 06:00 [entrez]
PHST- 2013/04/27 06:00 [pubmed]
PHST- 2013/11/12 06:00 [medline]
PHST- 2013/04/19 00:00 [pmc-release]
AID - PONE-D-12-36248 [pii]
AID - 10.1371/journal.pone.0061468 [doi]
PST - epublish
SO  - PLoS One. 2013 Apr 19;8(4):e61468. doi: 10.1371/journal.pone.0061468. Print 2013.